OOOkay, this is a massive can of worms to be opening…
Firstly, part of the way that ACT behaves by default is predicated on the notion that cortical columns can’t be reliably tracked, and therefore streamlines are just terminated at the interface because anything beyond that is not trustworthy. Now that’s not to say that ACT can’t track through the cortex, in fact it only requires a very simple tweak: Take the “cortical GM” volume from the 5TT image, add those partial volume fractions to the “Sub-cortical GM” volume, and then null the “Cortical GM” volume (they’re really more like “GM-you-can’t-track-through” and “GM-you-can-track-through” images…). That’s not to say this is a solution specifically for what you’ve asked here, I just wanted to explain that in case it’s relevant for yourself or anyone else interested in cortical columns.
If you have a specific interest in attempting to track cortical columns, I would definitely advocate pursuing an acquisition that is at least closer to isotropic. With the acquisition you have, your ability to track any curvature within the columns themselves will depend strongly on the orientation of those columns relative to the DWI voxel grid.
Furthermore, can you provide the resolution of any anatomical-contrast image you have? The reality is that in an experiment such as this, your results are going to depend exceptionally strongly on the quality of your tissue segmentation: fibre orientations from the diffusion model will only influence the angle that the streamlines take as they traverse that small 1-3mm gap between GM-pial and GM-WM interfaces. Use of binary masks to represent the tissues rather than partial volume fractions or native surface representations will be far more detrimental here than it would be in a typical tracking experiment, as the fraction of the streamlines lengths where the presence or absence of sub-voxel interpolation has an effect is greatly increased with such short streamlines.
I would suggest considering: should streamlines be permitted to terminate halfway across the depth of the cortical ribbon, or do you want to enforce that all streamlines must span from one interface to the other? This influences how you may wish to define your priors using image information.
Okay, final thought: ACT can be hacked. The algorithms provided in the
5ttgen script are not the only way to derive a 5TT image, and you can change the information contained within the 5TT image to alter what biological priors are applied where. So for instance: You could re-map the cortical segmentation to volume 2 (“WM”), and re-map both the CSF and WM segmentations to volume 0 (“Cortical GM”). Then, streamlines would only be permitted to exist within the cortical ribbon, and a streamline would need to reach either CSF or WM at both endpoints in order to be deemed satisfactory according to your imposed priors. Just food for thought…